Can I remove outliers with this command?

[Dicarlis]

My doubt is whether it is correct for me to consider the two factors of my experiment to remove the autiliers or do I just consider the main factor, follow the model I thought of using:

proc glm;

class BLOC factor1 factor2;

model GMDadap = BLOC  factor1 factor1 * factor2;

output out = dois residual = x_res student = x_stu ;

run;

proc print;

run;

proc means n mean;

class factor1;

var gmdadap;

run;

proc univariate normal plot data=dois;

histogram x_stu/normal;

var x_stu ;

run;

[ballardw]

Outliers for what variable(s)?

Remove for which step?

What constitutes an "outlier", as in rule(s), for each variable?

[Dicarlis]

Hello, thank you for your response. In our case, the variables are related to the performance of confined cattle (for example, average weight gain over 96 days). What we do is transform the residues to the Student scale (studentized residues) and we consider outiliers to be studentized residues greater than 3 or smaller than -3. however, we only performed this procedure if the residuals were not within normal limits using the Shapirowilk test, also performed for studentized residuals.

[PaigeMiller]

This is a reasonable way to approach outlier detection. Of course, there are plenty of other methods, including methods if the data is not normally distributed (such as box plot outliers), and even multivariate outlier detection. And possibly dozens of other methods.

[Dicarlis]

Hello, thanks for the answers, this question was generated because some colleagues, when working with experiments in a factorial arrangement, only consider the main factor to remove outliers, however I believe that the interaction between the factors (which makes up our treatments) that must be considered, is this line of thinking right?

[PaigeMiller]

There's no universally agreed upon method of detecting outliers. I think if you are going to fit a model with two factors, the outliers in Y ought to be detected via residuals, which means to me that all terms in the model should be used.